Ab-initio calculations, using quantum mechanical principles to calculate properties of materials, have been used for some time to predict features of materials. However, such calculations do not make use of the wealth of computed and measured information obtained on materials, in studying a system that has not previously been investigated or for which certain compositions or properties have not been investigated.
Prior to this work, no algorithms to extract knowledge and mathematical rules from the body of existing data on solid materials have been identified, limiting the usefulness of such data in predicting unknown properties of materials. Only simple heuristic models currently exist, in which visual correlations between properties, or few-parameter fits with pre-conceived (and therefore limited) models, are used to make predictions. Examples of heuristic models are Miedema's rules for compound formation in alloys, or Pettifor maps for making predictions of the structure of a new binary compound.
A number of problems in trying to predict structure information about new compounds have been observed, including difficult and time consuming calculations in ab-initio methods, and difficulties in extracting rules for use in heuristic models.
There is a need for systems and methods that combine information already known in a mathematical framework which can either predict directly attributes of materials or points at a few ab-initio calculations, which, when performed, will give the attribute of interest for the material.